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ORCIDJascha Sohl-Dickstein
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- affiliation: Google Brain, Mountain View, CA, USA
- affiliation (PhD 2012): UC Berkeley, Redwood Center for Theoretical Neuroscience, CA, USA
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2020 – today
- 2021
[i62]Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR abs/2101.07367 (2021)- 2020
- [c43]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. ICLR 2020 - [c42]Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak:
Infinite attention: NNGP and NTK for deep attention networks. ICML 2020: 4376-4386 - [c41]Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio:
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling. NeurIPS 2020 - [c40]Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS 2020 - [i61]Jascha Sohl-Dickstein, Roman Novak, Samuel S. Schoenholz, Jaehoon Lee:
On the infinite width limit of neural networks with a standard parameterization. CoRR abs/2001.07301 (2020) - [i60]Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein:
Using a thousand optimization tasks to learn hyperparameter search strategies. CoRR abs/2002.11887 (2020) - [i59]Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, Guy Gur-Ari:
The large learning rate phase of deep learning: the catapult mechanism. CoRR abs/2003.02218 (2020) - [i58]Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio:
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling. CoRR abs/2003.06060 (2020) - [i57]Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak:
Infinite attention: NNGP and NTK for deep attention networks. CoRR abs/2006.10540 (2020) - [i56]Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein:
Exact posterior distributions of wide Bayesian neural networks. CoRR abs/2006.10541 (2020) - [i55]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
A new method for parameter estimation in probabilistic models: Minimum probability flow. CoRR abs/2007.09240 (2020) - [i54]Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. CoRR abs/2007.15801 (2020) - [i53]Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein:
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible. CoRR abs/2008.07545 (2020) - [i52]Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein:
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves. CoRR abs/2009.11243 (2020) - [i51]Vinay Rao, Jascha Sohl-Dickstein:
Is Batch Norm unique? An empirical investigation and prescription to emulate the best properties of common normalizers without batch dependence. CoRR abs/2010.10687 (2020) - [i50]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. CoRR abs/2011.02159 (2020) - [i49]Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein:
Towards NNGP-guided Neural Architecture Search. CoRR abs/2011.06006 (2020) - [i48]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR abs/2011.13456 (2020) - [i47]Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, Pieter Abbeel:
Parallel Training of Deep Networks with Local Updates. CoRR abs/2012.03837 (2020)
2010 – 2019
- 2019
- [j4]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. J. Mach. Learn. Res. 20: 112:1-112:49 (2019) - [c39]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. DGS@ICLR 2019 - [c38]Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Reprogramming of Neural Networks. ICLR (Poster) 2019 - [c37]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR 2019 - [c36]Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. ICLR (Poster) 2019 - [c35]Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
A Mean Field Theory of Batch Normalization. ICLR (Poster) 2019 - [c34]Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML 2019: 4264-4273 - [c33]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Understanding and correcting pathologies in the training of learned optimizers. ICML 2019: 4556-4565 - [c32]Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith:
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. ICML 2019: 5042-5051 - [c31]Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth:
Invertible Convolutional Flow. NeurIPS 2019: 5636-5646 - [c30]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. NeurIPS 2019: 8570-8581 - [i46]Jascha Sohl-Dickstein, Kenji Kawaguchi:
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit. CoRR abs/1901.03909 (2019) - [i45]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. CoRR abs/1902.06720 (2019) - [i44]Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
A Mean Field Theory of Batch Normalization. CoRR abs/1902.08129 (2019) - [i43]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. CoRR abs/1903.07714 (2019) - [i42]Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith:
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. CoRR abs/1905.03776 (2019) - [i41]Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk:
Using learned optimizers to make models robust to input noise. CoRR abs/1906.03367 (2019) - [i40]Stephan Hoyer, Jascha Sohl-Dickstein, Sam Greydanus
:
Neural reparameterization improves structural optimization. CoRR abs/1909.04240 (2019) - [i39]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. CoRR abs/1912.02803 (2019) - 2018
- [c29]Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
Deep Neural Networks as Gaussian Processes. ICLR (Poster) 2018 - [c28]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. ICLR (Poster) 2018 - [c27]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning to Learn Without Labels. ICLR (Workshop) 2018 - [c26]Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Sensitivity and Generalization in Neural Networks: an Empirical Study. ICLR (Poster) 2018 - [c25]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. ICML 2018: 5389-5398 - [c24]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. NeurIPS 2018: 3914-3924 - [c23]Joseph M. Antognini, Jascha Sohl-Dickstein:
PCA of high dimensional random walks with comparison to neural network training. NeurIPS 2018: 10328-10337 - [i38]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Human and Computer Vision. CoRR abs/1802.08195 (2018) - [i37]Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Sensitivity and Generalization in Neural Networks: an Empirical Study. CoRR abs/1802.08760 (2018) - [i36]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning Unsupervised Learning Rules. CoRR abs/1804.00222 (2018) - [i35]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. CoRR abs/1806.05393 (2018) - [i34]Joseph M. Antognini, Jascha Sohl-Dickstein:
PCA of high dimensional random walks with comparison to neural network training. CoRR abs/1806.08805 (2018) - [i33]Samuel L. Smith, Daniel Duckworth, Quoc V. Le, Jascha Sohl-Dickstein:
Stochastic natural gradient descent draws posterior samples in function space. CoRR abs/1806.09597 (2018) - [i32]Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein:
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR abs/1806.10230 (2018) - [i31]Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Reprogramming of Neural Networks. CoRR abs/1806.11146 (2018) - [i30]Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. CoRR abs/1810.05148 (2018) - [i29]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR abs/1810.10180 (2018) - [i28]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. CoRR abs/1811.03600 (2018) - 2017
- [j3]Badr F. Albanna, Christopher Hillar, Jascha Sohl-Dickstein, Michael Robert DeWeese:
Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations. Entropy 19(8): 427 (2017) - [c22]Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo:
Capacity and Trainability in Recurrent Neural Networks. ICLR (Poster) 2017 - [c21]Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio:
Density estimation using Real NVP. ICLR (Poster) 2017 - [c20]Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein:
Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) 2017 - [c19]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. ICLR (Poster) 2017 - [c18]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. ICLR (Poster) 2017 - [c17]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) 2017 - [c16]Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo:
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. ICML 2017: 1136-1145 - [c15]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
On the Expressive Power of Deep Neural Networks. ICML 2017: 2847-2854 - [c14]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. ICML 2017: 3751-3760 - [c13]George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 2017: 2627-2636 - [c12]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. NIPS 2017: 6076-6085 - [i27]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. CoRR abs/1703.04813 (2017) - [i26]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR abs/1703.07370 (2017) - [i25]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. CoRR abs/1706.05806 (2017) - [i24]Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
A Correspondence Between Random Neural Networks and Statistical Field Theory. CoRR abs/1710.06570 (2017) - [i23]Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
Deep Neural Networks as Gaussian Processes. CoRR abs/1711.00165 (2017) - [i22]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. CoRR abs/1711.09268 (2017) - 2016
- [c11]Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli:
Exponential expressivity in deep neural networks through transient chaos. NIPS 2016: 3360-3368 - [i21]Subhaneil Lahiri, Jascha Sohl-Dickstein, Surya Ganguli:
A universal tradeoff between power, precision and speed in physical communication. CoRR abs/1603.07758 (2016) - [i20]Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio:
Density estimation using Real NVP. CoRR abs/1605.08803 (2016) - [i19]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
On the expressive power of deep neural networks. CoRR abs/1606.05336 (2016) - [i18]Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli:
Exponential expressivity in deep neural networks through transient chaos. CoRR abs/1606.05340 (2016) - [i17]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. CoRR abs/1611.01232 (2016) - [i16]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. CoRR abs/1611.02163 (2016) - [i15]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
Survey of Expressivity in Deep Neural Networks. CoRR abs/1611.08083 (2016) - [i14]Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo:
Intelligible Language Modeling with Input Switched Affine Networks. CoRR abs/1611.09434 (2016) - [i13]Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo:
Capacity and Trainability in Recurrent Neural Networks. CoRR abs/1611.09913 (2016) - [i12]Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein, Anelia Angelova:
Improved generator objectives for GANs. CoRR abs/1612.02780 (2016) - 2015
- [j2]Jascha Sohl-Dickstein, Santani Teng, Benjamin M. Gaub, Chris C. Rodgers, Crystal Li, Michael Robert DeWeese, Nicol S. Harper:
A Device for Human Ultrasonic Echolocation. IEEE Trans. Biomed. Eng. 62(6): 1526-1534 (2015) - [c10]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. ICML 2015: 2256-2265 - [c9]Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. NIPS 2015: 505-513 - [i11]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. CoRR abs/1503.03585 (2015) - [i10]Jascha Sohl-Dickstein, Diederik P. Kingma:
Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models. CoRR abs/1504.08025 (2015) - [i9]Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. CoRR abs/1506.05908 (2015) - 2014
- [j1]Urs Köster, Jascha Sohl-Dickstein, Charles M. Gray, Bruno A. Olshausen:
Modeling Higher-Order Correlations within Cortical Microcolumns. PLoS Comput. Biol. 10(7) (2014) - [c8]Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli:
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. ICML 2014: 604-612 - [c7]Jascha Sohl-Dickstein, Mayur Mudigonda, Michael Robert DeWeese:
Hamiltonian Monte Carlo Without Detailed Balance. ICML 2014: 719-726 - [i8]Ben Poole, Jascha Sohl-Dickstein, Surya Ganguli:
Analyzing noise in autoencoders and deep networks. CoRR abs/1406.1831 (2014) - 2013
- [c6]Eliana Feasley, Chris Klaiber, James Irwin, Jace Kohlmeier, Jascha Sohl-Dickstein:
Controlled experiments on millions of students to personalize learning. AIED Workshops 2013 - [c5]Joseph Jay Williams, Dave Paunesku, Benjamin Heley, Jascha Sohl-Dickstein:
Measurably Increasing Motivation in MOOCs. AIED Workshops 2013 - [i7]Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli:
An adaptive low dimensional quasi-Newton sum of functions optimizer. CoRR abs/1311.2115 (2013) - 2012
- [b1]Jascha Sohl-Dickstein:
Efficient Methods for Unsupervised Learning of Probabilistic Models. University of California, Berkeley, USA, 2012 - [c4]Lucas Theis, Jascha Sohl-Dickstein, Matthias Bethge:
Training sparse natural image models with a fast Gibbs sampler of an extended state space. NIPS 2012: 1133-1141 - [i6]Jascha Sohl-Dickstein:
The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use. CoRR abs/1205.1828 (2012) - [i5]Jascha Sohl-Dickstein, Benjamin J. Culpepper:
Hamiltonian Annealed Importance Sampling for partition function estimation. CoRR abs/1205.1925 (2012) - [i4]Jascha Sohl-Dickstein:
Hamiltonian Monte Carlo with Reduced Momentum Flips. CoRR abs/1205.1939 (2012) - [i3]Jascha Sohl-Dickstein:
Efficient Methods for Unsupervised Learning of Probabilistic Models. CoRR abs/1205.4295 (2012) - 2011
- [c3]Ching Ming Wang, Jascha Sohl-Dickstein, Ivana Tosic, Bruno A. Olshausen:
Lie Group Transformation Models for Predictive Video Coding. DCC 2011: 83-92 - [c2]Benjamin J. Culpepper, Jascha Sohl-Dickstein, Bruno A. Olshausen:
Building a better probabilistic model of images by factorization. ICCV 2011: 2011-2017 - [c1]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
Minimum Probability Flow Learning. ICML 2011: 905-912 - 2010
- [i2]Jascha Sohl-Dickstein, Jimmy C. Wang, Bruno A. Olshausen:
An Unsupervised Algorithm For Learning Lie Group Transformations. CoRR abs/1001.1027 (2010)
2000 – 2009
- 2009
- [i1]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
Minimum Probability Flow Learning. CoRR abs/0906.4779 (2009)
Coauthor Index
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